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Proceedings Paper

Recurrent neural networks for radar target identication
Author(s): Eric T. Kouba; Steven K. Rogers; Dennis W. Ruck; Kenneth W. Bauer
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Paper Abstract

A real-time recurrent learning algorithm was applied to a five class radar target identification problem. Wideband radar signatures were generated for five aircraft classes. Since an aircraft in flight is constantly in motion, a radar can measure sequences of radar signatures as the aspect angle changes. A radar can also generate aspect angle estimates by using kinematic information from aircraft position and velocity measurements. A recurrent neural network computer program (implementing a real time recurrent learning algorithm) was trained to recognize these sequences of radar signatures. Each radar signature was described by 6 external input features: the estimated target azimuth, the estimated target width, and 4 noisy amplitude values from 2 peak range bins. Nine consecutive radar signatures were sufficient to achieve a test set accuracy of 96%.

Paper Details

Date Published: 2 September 1993
PDF: 11 pages
Proc. SPIE 1965, Applications of Artificial Neural Networks IV, (2 September 1993); doi: 10.1117/12.152541
Show Author Affiliations
Eric T. Kouba, Air Force Institute of Technology (United States)
Steven K. Rogers, Air Force Institute of Technology (United States)
Dennis W. Ruck, Air Force Institute of Technology (United States)
Kenneth W. Bauer, Air Force Institute of Technology (United States)

Published in SPIE Proceedings Vol. 1965:
Applications of Artificial Neural Networks IV
Steven K. Rogers, Editor(s)

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